Sammenlign metoder

	Robust Path Analysis	Robust konfirmatorisk faktoranalyse
Fagområde	Statistik	Statistik
Familie	Latent structure	Latent structure
Oprindelsesår	1998	1984–1994
Ophavsperson	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)
Type	Causal path modeling with robust estimation	Confirmatory latent variable model with robust estimation
Oprindelig kilde	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.	Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.
Aliasser	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA
Relaterede	6	6
Resumé	Robust path analysis applies robust estimation — such as sandwich standard errors or M-estimation — to path models that specify directed causal relationships among observed variables. It preserves valid inference about path coefficients and indirect effects when data violate normality, contain outliers, or exhibit heteroscedasticity that would distort conventional standard errors.	Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.